Overview

Dataset statistics

Number of variables18
Number of observations25
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.5 KiB
Average record size in memory145.1 B

Variable types

Numeric18

Alerts

population is highly correlated with GDP_constant_2010_USD and 6 other fieldsHigh correlation
GDP_constant_2010_USD is highly correlated with population and 11 other fieldsHigh correlation
land_area_km_sq is highly correlated with population and 5 other fieldsHigh correlation
population_% is highly correlated with population and 6 other fieldsHigh correlation
GDP_% is highly correlated with population and 11 other fieldsHigh correlation
land_area_% is highly correlated with population and 5 other fieldsHigh correlation
EF.EFM.OVRL.XD is highly correlated with GDP_constant_2010_USD and 7 other fieldsHigh correlation
EF.EFM.RANK.XD is highly correlated with GDP_constant_2010_USD and 6 other fieldsHigh correlation
1.1_ACCESS.ELECTRICITY.TOT is highly correlated with 1.2_ACCESS.ELECTRICITY.RURALHigh correlation
1.2_ACCESS.ELECTRICITY.RURAL is highly correlated with 1.1_ACCESS.ELECTRICITY.TOTHigh correlation
SH.UHC.NOP1.CG is highly correlated with population and 3 other fieldsHigh correlation
SH.UHC.NOP2.TO is highly correlated with population and 6 other fieldsHigh correlation
SH.UHC.OOPC.25.ZS is highly correlated with EF.EFM.OVRL.XD and 3 other fieldsHigh correlation
CC.EST is highly correlated with GDP_constant_2010_USD and 5 other fieldsHigh correlation
GE.EST is highly correlated with GDP_constant_2010_USD and 5 other fieldsHigh correlation
RQ.EST is highly correlated with GDP_constant_2010_USD and 6 other fieldsHigh correlation
VA.EST is highly correlated with GDP_constant_2010_USD and 6 other fieldsHigh correlation
population is highly correlated with GDP_constant_2010_USD and 8 other fieldsHigh correlation
GDP_constant_2010_USD is highly correlated with population and 10 other fieldsHigh correlation
land_area_km_sq is highly correlated with population and 4 other fieldsHigh correlation
population_% is highly correlated with population and 8 other fieldsHigh correlation
GDP_% is highly correlated with population and 10 other fieldsHigh correlation
land_area_% is highly correlated with population and 4 other fieldsHigh correlation
EF.EFM.OVRL.XD is highly correlated with population and 5 other fieldsHigh correlation
EF.EFM.RANK.XD is highly correlated with GDP_constant_2010_USD and 3 other fieldsHigh correlation
1.1_ACCESS.ELECTRICITY.TOT is highly correlated with 1.2_ACCESS.ELECTRICITY.RURAL and 2 other fieldsHigh correlation
1.2_ACCESS.ELECTRICITY.RURAL is highly correlated with 1.1_ACCESS.ELECTRICITY.TOT and 2 other fieldsHigh correlation
SH.UHC.NOP1.CG is highly correlated with population and 5 other fieldsHigh correlation
SH.UHC.NOP2.TO is highly correlated with population and 5 other fieldsHigh correlation
SH.UHC.OOPC.25.ZS is highly correlated with population and 5 other fieldsHigh correlation
CC.EST is highly correlated with GE.EST and 2 other fieldsHigh correlation
GE.EST is highly correlated with 1.1_ACCESS.ELECTRICITY.TOT and 4 other fieldsHigh correlation
RQ.EST is highly correlated with 1.1_ACCESS.ELECTRICITY.TOT and 4 other fieldsHigh correlation
VA.EST is highly correlated with GDP_constant_2010_USD and 6 other fieldsHigh correlation
population is highly correlated with GDP_constant_2010_USD and 5 other fieldsHigh correlation
GDP_constant_2010_USD is highly correlated with population and 4 other fieldsHigh correlation
land_area_km_sq is highly correlated with population and 4 other fieldsHigh correlation
population_% is highly correlated with population and 5 other fieldsHigh correlation
GDP_% is highly correlated with population and 6 other fieldsHigh correlation
land_area_% is highly correlated with population and 4 other fieldsHigh correlation
EF.EFM.OVRL.XD is highly correlated with GDP_% and 1 other fieldsHigh correlation
EF.EFM.RANK.XD is highly correlated with GDP_% and 1 other fieldsHigh correlation
1.1_ACCESS.ELECTRICITY.TOT is highly correlated with 1.2_ACCESS.ELECTRICITY.RURALHigh correlation
1.2_ACCESS.ELECTRICITY.RURAL is highly correlated with 1.1_ACCESS.ELECTRICITY.TOTHigh correlation
SH.UHC.NOP1.CG is highly correlated with population and 3 other fieldsHigh correlation
SH.UHC.NOP2.TO is highly correlated with SH.UHC.NOP1.CGHigh correlation
SH.UHC.OOPC.25.ZS is highly correlated with SH.UHC.NOP1.CG and 1 other fieldsHigh correlation
CC.EST is highly correlated with GE.ESTHigh correlation
GE.EST is highly correlated with CC.EST and 1 other fieldsHigh correlation
RQ.EST is highly correlated with GE.ESTHigh correlation
VA.EST is highly correlated with SH.UHC.OOPC.25.ZSHigh correlation
population is highly correlated with GDP_constant_2010_USD and 9 other fieldsHigh correlation
GDP_constant_2010_USD is highly correlated with population and 8 other fieldsHigh correlation
land_area_km_sq is highly correlated with population and 5 other fieldsHigh correlation
population_% is highly correlated with population and 12 other fieldsHigh correlation
GDP_% is highly correlated with population and 10 other fieldsHigh correlation
land_area_% is highly correlated with population and 9 other fieldsHigh correlation
EF.EFM.OVRL.XD is highly correlated with GDP_constant_2010_USD and 10 other fieldsHigh correlation
EF.EFM.RANK.XD is highly correlated with population_% and 9 other fieldsHigh correlation
1.1_ACCESS.ELECTRICITY.TOT is highly correlated with population_% and 7 other fieldsHigh correlation
1.2_ACCESS.ELECTRICITY.RURAL is highly correlated with population_% and 7 other fieldsHigh correlation
SH.UHC.NOP1.CG is highly correlated with population and 7 other fieldsHigh correlation
SH.UHC.NOP2.TO is highly correlated with population and 6 other fieldsHigh correlation
SH.UHC.OOPC.25.ZS is highly correlated with GDP_constant_2010_USD and 7 other fieldsHigh correlation
CC.EST is highly correlated with population and 5 other fieldsHigh correlation
GE.EST is highly correlated with EF.EFM.OVRL.XD and 6 other fieldsHigh correlation
PV.EST is highly correlated with population and 8 other fieldsHigh correlation
RQ.EST is highly correlated with population and 7 other fieldsHigh correlation
VA.EST is highly correlated with population_% and 6 other fieldsHigh correlation
population has unique values Unique
GDP_constant_2010_USD has unique values Unique
population_% has unique values Unique
GDP_% has unique values Unique
land_area_% has unique values Unique
EF.EFM.OVRL.XD has unique values Unique
1.1_ACCESS.ELECTRICITY.TOT has unique values Unique
1.2_ACCESS.ELECTRICITY.RURAL has unique values Unique
CC.EST has unique values Unique
GE.EST has unique values Unique
PV.EST has unique values Unique
RQ.EST has unique values Unique
VA.EST has unique values Unique

Reproduction

Analysis started2023-01-06 11:08:46.291312
Analysis finished2023-01-06 11:09:02.926712
Duration16.64 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

population
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct25
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42902328.4
Minimum2908220
Maximum113661809
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size328.0 B
2023-01-06T12:09:02.952928image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum2908220
5-th percentile3925714.8
Q115012985
median35074931
Q365002231
95-th percentile104733956.8
Maximum113661809
Range110753589
Interquartile range (IQR)49989246

Descriptive statistics

Standard deviation33561575.33
Coefficient of variation (CV)0.7822786449
Kurtosis-0.4901808354
Mean42902328.4
Median Absolute Deviation (MAD)25076541
Skewness0.7371727844
Sum1072558210
Variance1.126379338 × 1015
MonotonicityNot monotonic
2023-01-06T12:09:03.008442image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
29082201
 
4.0%
370574521
 
4.0%
956874521
 
4.0%
73502221
 
4.0%
315968551
 
4.0%
427241631
 
4.0%
189070081
 
4.0%
678439791
 
4.0%
77630001
 
4.0%
314504831
 
4.0%
Other values (15)15
60.0%
ValueCountFrequency (%)
29082201
4.0%
30695881
4.0%
73502221
4.0%
77630001
4.0%
92560371
4.0%
104073361
4.0%
150129851
4.0%
189070081
4.0%
221135481
4.0%
314504831
4.0%
ValueCountFrequency (%)
1136618091
4.0%
1069955831
4.0%
956874521
4.0%
924441831
4.0%
753818991
4.0%
678439791
4.0%
650022311
4.0%
601514721
4.0%
510571891
4.0%
427241631
4.0%

GDP_constant_2010_USD
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct25
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9668752 × 1011
Minimum4310688308
Maximum1.04935 × 1012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size328.0 B
2023-01-06T12:09:03.066351image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum4310688308
5-th percentile9684518663
Q12.619918998 × 1010
median8.613522124 × 1010
Q32.21895 × 1011
95-th percentile8.874056 × 1011
Maximum1.04935 × 1012
Range1.045039312 × 1012
Interquartile range (IQR)1.9569581 × 1011

Descriptive statistics

Standard deviation2.880572608 × 1011
Coefficient of variation (CV)1.464542645
Kurtosis3.62293912
Mean1.9668752 × 1011
Median Absolute Deviation (MAD)6.32054784 × 1010
Skewness2.084397003
Sum4.917188001 × 1012
Variance8.297698551 × 1022
MonotonicityNot monotonic
2023-01-06T12:09:03.121981image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
1.055438901 × 10101
 
4.0%
3.0323 × 10111
 
4.0%
7.2758 × 10111
 
4.0%
4.049280512 × 10101
 
4.0%
8.613522124 × 10101
 
4.0%
2.19601 × 10111
 
4.0%
2.473481904 × 10101
 
4.0%
3.68884 × 10111
 
4.0%
94670510761
 
4.0%
2.619918998 × 10101
 
4.0%
Other values (15)15
60.0%
ValueCountFrequency (%)
43106883081
4.0%
94670510761
4.0%
1.055438901 × 10101
4.0%
2.292974284 × 10101
4.0%
2.375218115 × 10101
4.0%
2.473481904 × 10101
4.0%
2.619918998 × 10101
4.0%
2.63180654 × 10101
4.0%
2.9576212 × 10101
4.0%
3.615785926 × 10101
4.0%
ValueCountFrequency (%)
1.04935 × 10121
4.0%
9.27362 × 10111
4.0%
7.2758 × 10111
4.0%
3.68884 × 10111
4.0%
3.0323 × 10111
4.0%
2.72361 × 10111
4.0%
2.21895 × 10111
4.0%
2.19601 × 10111
4.0%
1.5524 × 10111
4.0%
1.0106 × 10111
4.0%

land_area_km_sq
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct17
Distinct (%)68.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean793922.68
Minimum28470
Maximum2736690
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size228.0 B
2023-01-06T12:09:03.175663image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum28470
5-th percentile39419.4
Q1155360
median510890
Q31000000
95-th percentile2548550
Maximum2736690
Range2708220
Interquartile range (IQR)844640

Descriptive statistics

Standard deviation818694.7987
Coefficient of variation (CV)1.03120218
Kurtosis0.7093470149
Mean793922.68
Median Absolute Deviation (MAD)390480
Skewness1.32089574
Sum19848067
Variance6.702611735 × 1011
MonotonicityNot monotonic
2023-01-06T12:09:03.227768image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
19439503
12.0%
5108903
12.0%
284702
 
8.0%
4463002
 
8.0%
1553602
 
8.0%
5691402
 
8.0%
1204101
 
4.0%
874601
 
4.0%
11095001
 
4.0%
4727101
 
4.0%
Other values (7)7
28.0%
ValueCountFrequency (%)
284702
8.0%
832171
 
4.0%
874601
 
4.0%
1204101
 
4.0%
1553602
8.0%
2005201
 
4.0%
4463002
8.0%
4727101
 
4.0%
5108903
12.0%
5691402
8.0%
ValueCountFrequency (%)
27366901
 
4.0%
26997001
 
4.0%
19439503
12.0%
11095001
 
4.0%
10000001
 
4.0%
9954501
 
4.0%
5793501
 
4.0%
5691402
8.0%
5108903
12.0%
4727101
 
4.0%

population_%
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct25
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.006672295817
Minimum0.0004307961273
Maximum0.01683678234
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size328.0 B
2023-01-06T12:09:03.289093image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.0004307961273
5-th percentile0.0006206469527
Q10.002336575341
median0.005159910096
Q30.0101167496
95-th percentile0.01662242185
Maximum0.01683678234
Range0.01640598621
Interquartile range (IQR)0.007780174259

Descriptive statistics

Standard deviation0.005198312304
Coefficient of variation (CV)0.7790890043
Kurtosis-0.5277428397
Mean0.006672295817
Median Absolute Deviation (MAD)0.003645403288
Skewness0.7231579906
Sum0.1668073954
Variance2.702245081 × 10-5
MonotonicityNot monotonic
2023-01-06T12:09:03.342466image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
0.00043079612731
 
4.0%
0.0060798150761
 
4.0%
0.01650224031
 
4.0%
0.0010887921731
 
4.0%
0.0046804584141
 
4.0%
0.0066494588331
 
4.0%
0.0028007048391
 
4.0%
0.0095330860151
 
4.0%
0.0013388055461
 
4.0%
0.0051599100961
 
4.0%
Other values (15)15
60.0%
ValueCountFrequency (%)
0.00043079612731
4.0%
0.00050361064761
4.0%
0.0010887921731
4.0%
0.0013388055461
4.0%
0.0015416440451
4.0%
0.0015962944311
4.0%
0.0023365753411
4.0%
0.0028007048391
4.0%
0.0038136984031
4.0%
0.0046804584141
4.0%
ValueCountFrequency (%)
0.016836782341
4.0%
0.016652467241
4.0%
0.01650224031
4.0%
0.01298977981
4.0%
0.011732209571
4.0%
0.010373711751
4.0%
0.01011674961
4.0%
0.0095330860151
4.0%
0.0088053133841
4.0%
0.0066494588331
4.0%

GDP_%
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct25
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.00357105507
Minimum8.609585763 × 10-5
Maximum0.01660862798
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size328.0 B
2023-01-06T12:09:03.402635image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum8.609585763 × 10-5
5-th percentile0.0001746892288
Q10.0005232671836
median0.001341150589
Q30.004871364702
95-th percentile0.01653694547
Maximum0.01660862798
Range0.01652253212
Interquartile range (IQR)0.004348097518

Descriptive statistics

Standard deviation0.005204080445
Coefficient of variation (CV)1.457294929
Kurtosis2.822192494
Mean0.00357105507
Median Absolute Deviation (MAD)0.001013065992
Skewness1.967281287
Sum0.08927637674
Variance2.708245327 × 10-5
MonotonicityNot monotonic
2023-01-06T12:09:03.454900image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
0.0001643349241
 
4.0%
0.0060563058711
 
4.0%
0.016608627981
 
4.0%
0.00063048481961
 
4.0%
0.0013411505891
 
4.0%
0.0039277156421
 
4.0%
0.00038512836711
 
4.0%
0.0052780861641
 
4.0%
0.0002161064481
 
4.0%
0.00052326718361
 
4.0%
Other values (15)15
60.0%
ValueCountFrequency (%)
8.609585763 × 10-51
4.0%
0.0001643349241
4.0%
0.0002161064481
4.0%
0.00032808459681
4.0%
0.00038512836711
4.0%
0.00051735585361
4.0%
0.00052326718361
4.0%
0.00052899099051
4.0%
0.00054219624031
4.0%
0.00060076824191
4.0%
ValueCountFrequency (%)
0.016608627981
4.0%
0.016586510231
4.0%
0.016338686431
4.0%
0.0060563058711
4.0%
0.0052780861641
4.0%
0.0050652457551
4.0%
0.0048713647021
4.0%
0.0039277156421
4.0%
0.002776574681
4.0%
0.0020292278121
4.0%

land_area_%
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct25
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.006120175599
Minimum0.0002203179726
Maximum0.02127976593
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size328.0 B
2023-01-06T12:09:03.512847image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.0002203179726
5-th percentile0.0003040767511
Q10.001202269063
median0.003897708665
Q30.007703629796
95-th percentile0.01972282457
Maximum0.02127976593
Range0.02105944796
Interquartile range (IQR)0.006501360733

Descriptive statistics

Standard deviation0.006337826076
Coefficient of variation (CV)1.035562783
Kurtosis0.755757269
Mean0.006120175599
Median Absolute Deviation (MAD)0.002979070406
Skewness1.333506395
Sum0.15300439
Variance4.016803937 × 10-5
MonotonicityNot monotonic
2023-01-06T12:09:03.564751image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
0.00022031797261
 
4.0%
0.021279765931
 
4.0%
0.014830884851
 
4.0%
0.00067681805011
 
4.0%
0.003453737661
 
4.0%
0.0085862446721
 
4.0%
0.0036581141141
 
4.0%
0.0038715249941
 
4.0%
0.00063488348171
 
4.0%
0.0044254796781
 
4.0%
Other values (15)15
60.0%
ValueCountFrequency (%)
0.00022031797261
4.0%
0.00022137506841
4.0%
0.00063488348171
4.0%
0.00067681805011
4.0%
0.00091863825931
4.0%
0.0011852806241
4.0%
0.0012022690631
4.0%
0.0015195407851
4.0%
0.0033820619011
4.0%
0.003453737661
4.0%
ValueCountFrequency (%)
0.021279765931
4.0%
0.020892550471
4.0%
0.015043920981
4.0%
0.015043453561
4.0%
0.014830884851
4.0%
0.0085862446721
4.0%
0.0077036297961
4.0%
0.0075780011231
4.0%
0.0044254796781
4.0%
0.0044200072711
4.0%

EF.EFM.OVRL.XD
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct25
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.94167804
Minimum0.035961
Maximum2.4083
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size328.0 B
2023-01-06T12:09:03.620477image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.035961
5-th percentile0.150976
Q10.44222
median0.70816
Q31.5323
95-th percentile2.06912
Maximum2.4083
Range2.372339
Interquartile range (IQR)1.09008

Descriptive statistics

Standard deviation0.6557365713
Coefficient of variation (CV)0.6963490104
Kurtosis-0.55425019
Mean0.94167804
Median Absolute Deviation (MAD)0.37301
Skewness0.6684211426
Sum23.541951
Variance0.4299904509
MonotonicityNot monotonic
2023-01-06T12:09:03.676092image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
0.509171
 
4.0%
0.905671
 
4.0%
1.67761
 
4.0%
1.27731
 
4.0%
0.708161
 
4.0%
0.562081
 
4.0%
0.0359611
 
4.0%
2.40831
 
4.0%
0.400061
 
4.0%
0.387331
 
4.0%
Other values (15)15
60.0%
ValueCountFrequency (%)
0.0359611
4.0%
0.133491
4.0%
0.220921
4.0%
0.335151
4.0%
0.387331
4.0%
0.400061
4.0%
0.442221
4.0%
0.509171
4.0%
0.538711
4.0%
0.562081
4.0%
ValueCountFrequency (%)
2.40831
4.0%
2.13671
4.0%
1.79881
4.0%
1.67761
4.0%
1.61141
4.0%
1.58691
4.0%
1.53231
4.0%
1.33061
4.0%
1.27731
4.0%
0.987971
4.0%

EF.EFM.RANK.XD
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct21
Distinct (%)84.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean55.8
Minimum20
Maximum124
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size328.0 B
2023-01-06T12:09:03.729678image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile21.2
Q131
median59
Q371
95-th percentile96
Maximum124
Range104
Interquartile range (IQR)40

Descriptive statistics

Standard deviation26.3264759
Coefficient of variation (CV)0.4718006434
Kurtosis0.2908637685
Mean55.8
Median Absolute Deviation (MAD)20
Skewness0.6078403731
Sum1395
Variance693.0833333
MonotonicityNot monotonic
2023-01-06T12:09:03.880858image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
712
 
8.0%
202
 
8.0%
262
 
8.0%
602
 
8.0%
811
 
4.0%
491
 
4.0%
391
 
4.0%
621
 
4.0%
651
 
4.0%
1241
 
4.0%
Other values (11)11
44.0%
ValueCountFrequency (%)
202
8.0%
262
8.0%
281
4.0%
291
4.0%
311
4.0%
341
4.0%
391
4.0%
491
4.0%
511
4.0%
561
4.0%
ValueCountFrequency (%)
1241
4.0%
981
4.0%
881
4.0%
811
4.0%
741
4.0%
731
4.0%
712
8.0%
651
4.0%
621
4.0%
602
8.0%

1.1_ACCESS.ELECTRICITY.TOT
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct25
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean78.26041118
Minimum4.74979353
Maximum99.62101746
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size328.0 B
2023-01-06T12:09:03.935881image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum4.74979353
5-th percentile15.97357903
Q182.8352356
median95.17656708
Q398.914037
95-th percentile99.41251953
Maximum99.62101746
Range94.87122393
Interquartile range (IQR)16.0788014

Descriptive statistics

Standard deviation32.90570661
Coefficient of variation (CV)0.4204642694
Kurtosis0.2105598161
Mean78.26041118
Median Absolute Deviation (MAD)4.012832642
Skewness-1.391636255
Sum1956.510279
Variance1082.785527
MonotonicityNot monotonic
2023-01-06T12:09:03.989433image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
99.415649411
 
4.0%
95.099205021
 
4.0%
96.0936241
 
4.0%
99.621017461
 
4.0%
86.506950381
 
4.0%
94.948005681
 
4.0%
50.569175721
 
4.0%
99.108623551
 
4.0%
97.567489621
 
4.0%
15.91323281
 
4.0%
Other values (15)15
60.0%
ValueCountFrequency (%)
4.749793531
4.0%
15.91323281
4.0%
16.214963911
4.0%
20.53929711
4.0%
28.363447191
4.0%
50.569175721
4.0%
82.83523561
4.0%
86.506950381
4.0%
90.41
4.0%
91.302543641
4.0%
ValueCountFrequency (%)
99.621017461
4.0%
99.415649411
4.0%
99.41
4.0%
99.189399721
4.0%
99.108623551
4.0%
98.982971191
4.0%
98.9140371
4.0%
98.91
4.0%
98.5977011
4.0%
98.101348881
4.0%

1.2_ACCESS.ELECTRICITY.RURAL
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct25
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean72.41600486
Minimum4.74979353
Maximum99.61004008
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size328.0 B
2023-01-06T12:09:04.041759image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum4.74979353
5-th percentile7.934174593
Q173.69443659
median90.87485529
Q398.6
95-th percentile99.07409445
Maximum99.61004008
Range94.86024655
Interquartile range (IQR)24.90556341

Descriptive statistics

Standard deviation36.2082006
Coefficient of variation (CV)0.5000027365
Kurtosis-0.4963927564
Mean72.41600486
Median Absolute Deviation (MAD)7.968862493
Skewness-1.150399429
Sum1810.400122
Variance1311.033791
MonotonicityNot monotonic
2023-01-06T12:09:04.092684image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
99.116520961
 
4.0%
95.099205021
 
4.0%
86.3451061
 
4.0%
99.610040081
 
4.0%
73.694436591
 
4.0%
83.268169941
 
4.0%
16.562362271
 
4.0%
98.843717781
 
4.0%
95.746307331
 
4.0%
7.4227633521
 
4.0%
Other values (15)15
60.0%
ValueCountFrequency (%)
4.749793531
4.0%
7.4227633521
4.0%
9.9798195561
4.0%
11.027404731
4.0%
15.516442871
4.0%
16.562362271
4.0%
73.694436591
4.0%
75.210119031
4.0%
75.448639941
4.0%
83.268169941
4.0%
ValueCountFrequency (%)
99.610040081
4.0%
99.116520961
4.0%
98.904388431
4.0%
98.843717781
4.0%
98.734914851
4.0%
98.648740121
4.0%
98.61
4.0%
98.569369911
4.0%
95.9355021
4.0%
95.8685431
4.0%

SH.UHC.NOP1.CG
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct7
Distinct (%)28.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.000918492
Minimum0.0002538
Maximum0.006559
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size328.0 B
2023-01-06T12:09:04.141130image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.0002538
5-th percentile0.00044294
Q10.0005343
median0.0005343
Q30.0005343
95-th percentile0.00285766
Maximum0.006559
Range0.0063052
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.001304504131
Coefficient of variation (CV)1.420267276
Kurtosis15.81670179
Mean0.000918492
Median Absolute Deviation (MAD)0
Skewness3.879060388
Sum0.0229623
Variance1.701731027 × 10-6
MonotonicityNot monotonic
2023-01-06T12:09:04.185437image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0.000534319
76.0%
0.00145031
 
4.0%
0.00091791
 
4.0%
0.00320951
 
4.0%
0.00025381
 
4.0%
0.0065591
 
4.0%
0.00042011
 
4.0%
ValueCountFrequency (%)
0.00025381
 
4.0%
0.00042011
 
4.0%
0.000534319
76.0%
0.00091791
 
4.0%
0.00145031
 
4.0%
0.00320951
 
4.0%
0.0065591
 
4.0%
ValueCountFrequency (%)
0.0065591
 
4.0%
0.00320951
 
4.0%
0.00145031
 
4.0%
0.00091791
 
4.0%
0.000534319
76.0%
0.00042011
 
4.0%
0.00025381
 
4.0%

SH.UHC.NOP2.TO
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct7
Distinct (%)28.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean263740
Minimum14000
Maximum1809000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size328.0 B
2023-01-06T12:09:04.231481image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum14000
5-th percentile69500
Q169500
median69500
Q369500
95-th percentile1351800
Maximum1809000
Range1795000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation480680.7698
Coefficient of variation (CV)1.822555433
Kurtosis4.704017361
Mean263740
Median Absolute Deviation (MAD)0
Skewness2.381745658
Sum6593500
Variance2.310540025 × 1011
MonotonicityNot monotonic
2023-01-06T12:09:04.277682image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
6950019
76.0%
11390001
 
4.0%
7980001
 
4.0%
14050001
 
4.0%
140001
 
4.0%
18090001
 
4.0%
1080001
 
4.0%
ValueCountFrequency (%)
140001
 
4.0%
6950019
76.0%
1080001
 
4.0%
7980001
 
4.0%
11390001
 
4.0%
14050001
 
4.0%
18090001
 
4.0%
ValueCountFrequency (%)
18090001
 
4.0%
14050001
 
4.0%
11390001
 
4.0%
7980001
 
4.0%
1080001
 
4.0%
6950019
76.0%
140001
 
4.0%

SH.UHC.OOPC.25.ZS
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct7
Distinct (%)28.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.520987747
Minimum0.1422174
Maximum2.76697
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size328.0 B
2023-01-06T12:09:04.334968image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.1422174
5-th percentile1.439833014
Q11.439833014
median1.439833014
Q31.439833014
95-th percentile2.6030982
Maximum2.76697
Range2.6247526
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.4719140992
Coefficient of variation (CV)0.3102681795
Kurtosis5.583618027
Mean1.520987747
Median Absolute Deviation (MAD)0
Skewness0.5484505193
Sum38.02469366
Variance0.2227029171
MonotonicityNot monotonic
2023-01-06T12:09:04.381425image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1.43983301419
76.0%
1.509391
 
4.0%
2.0364511
 
4.0%
2.744761
 
4.0%
1.4680781
 
4.0%
2.766971
 
4.0%
0.14221741
 
4.0%
ValueCountFrequency (%)
0.14221741
 
4.0%
1.43983301419
76.0%
1.4680781
 
4.0%
1.509391
 
4.0%
2.0364511
 
4.0%
2.744761
 
4.0%
2.766971
 
4.0%
ValueCountFrequency (%)
2.766971
 
4.0%
2.744761
 
4.0%
2.0364511
 
4.0%
1.509391
 
4.0%
1.4680781
 
4.0%
1.43983301419
76.0%
0.14221741
 
4.0%

CC.EST
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct25
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.6493837517
Minimum-1.445619464
Maximum-0.1406314522
Zeros0
Zeros (%)0.0%
Negative25
Negative (%)100.0%
Memory size328.0 B
2023-01-06T12:09:04.435769image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-1.445619464
5-th percentile-1.256349993
Q1-1.001575112
median-0.5336781144
Q3-0.3611920476
95-th percentile-0.1669469059
Maximum-0.1406314522
Range1.304988012
Interquartile range (IQR)0.6403830647

Descriptive statistics

Standard deviation0.383090229
Coefficient of variation (CV)-0.5899288795
Kurtosis-0.9241135053
Mean-0.6493837517
Median Absolute Deviation (MAD)0.2875458747
Skewness-0.5022214781
Sum-16.23459379
Variance0.1467581236
MonotonicityNot monotonic
2023-01-06T12:09:04.494242image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
-0.71241235731
 
4.0%
-0.17240765691
 
4.0%
-0.51242864131
 
4.0%
-0.30246177321
 
4.0%
-0.43366572261
 
4.0%
-0.14063145221
 
4.0%
-1.0342283251
 
4.0%
-0.39740407471
 
4.0%
-1.4456194641
 
4.0%
-1.1443636421
 
4.0%
Other values (15)15
60.0%
ValueCountFrequency (%)
-1.4456194641
4.0%
-1.2843465811
4.0%
-1.1443636421
4.0%
-1.1101369861
4.0%
-1.1059536931
4.0%
-1.0342283251
4.0%
-1.0015751121
4.0%
-0.89788025621
4.0%
-0.84808623791
4.0%
-0.71241235731
4.0%
ValueCountFrequency (%)
-0.14063145221
4.0%
-0.16558171811
4.0%
-0.17240765691
4.0%
-0.24613223971
4.0%
-0.30246177321
4.0%
-0.30984988811
4.0%
-0.36119204761
4.0%
-0.38933485751
4.0%
-0.39740407471
4.0%
-0.43366572261
4.0%

GE.EST
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct25
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.3057484635
Minimum-1.890350342
Maximum0.3757234216
Zeros0
Zeros (%)0.0%
Negative17
Negative (%)68.0%
Memory size328.0 B
2023-01-06T12:09:04.553356image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-1.890350342
5-th percentile-0.8978005767
Q1-0.6200786233
median-0.2551155984
Q30.1395753771
95-th percentile0.3121770799
Maximum0.3757234216
Range2.266073763
Interquartile range (IQR)0.7596540004

Descriptive statistics

Standard deviation0.513692379
Coefficient of variation (CV)-1.680114344
Kurtosis2.241777395
Mean-0.3057484635
Median Absolute Deviation (MAD)0.3946909755
Skewness-1.11287432
Sum-7.643711587
Variance0.2638798603
MonotonicityNot monotonic
2023-01-06T12:09:04.604557image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
-0.17073829471
 
4.0%
-0.028038693591
 
4.0%
0.2253267021
 
4.0%
-0.25511559841
 
4.0%
-0.34774777291
 
4.0%
-0.21207855641
 
4.0%
-0.82344454531
 
4.0%
0.13957537711
 
4.0%
-0.91638958451
 
4.0%
-0.62007862331
 
4.0%
Other values (15)15
60.0%
ValueCountFrequency (%)
-1.8903503421
4.0%
-0.91638958451
4.0%
-0.82344454531
4.0%
-0.77865636351
4.0%
-0.68737626081
4.0%
-0.67008024451
4.0%
-0.62007862331
4.0%
-0.60115486381
4.0%
-0.5464175941
4.0%
-0.45027354361
4.0%
ValueCountFrequency (%)
0.37572342161
4.0%
0.32291823631
4.0%
0.26921245461
4.0%
0.2253267021
4.0%
0.18022455281
4.0%
0.16355502611
4.0%
0.13957537711
4.0%
0.078619517391
4.0%
-0.028038693591
4.0%
-0.13327500221
4.0%

PV.EST
Real number (ℝ)

HIGH CORRELATION
UNIQUE

Distinct25
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.5951106446
Minimum-2.271226406
Maximum0.4667991102
Zeros0
Zeros (%)0.0%
Negative20
Negative (%)80.0%
Memory size328.0 B
2023-01-06T12:09:04.661574image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-2.271226406
5-th percentile-1.494743204
Q1-0.8689665198
median-0.5712732077
Q3-0.152148813
95-th percentile0.2300018832
Maximum0.4667991102
Range2.738025516
Interquartile range (IQR)0.7168177068

Descriptive statistics

Standard deviation0.6103460159
Coefficient of variation (CV)-1.025600905
Kurtosis1.100658426
Mean-0.5951106446
Median Absolute Deviation (MAD)0.357190758
Skewness-0.6055421193
Sum-14.87776612
Variance0.3725222591
MonotonicityNot monotonic
2023-01-06T12:09:04.710604image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
-0.0068530277351
 
4.0%
0.095823876561
 
4.0%
-0.92545914651
 
4.0%
-0.54271298651
 
4.0%
-0.57127320771
 
4.0%
-2.2712264061
 
4.0%
-0.55211091041
 
4.0%
-1.2166306971
 
4.0%
-0.82892954351
 
4.0%
-1.0683526991
 
4.0%
Other values (15)15
60.0%
ValueCountFrequency (%)
-2.2712264061
4.0%
-1.5642713311
4.0%
-1.2166306971
4.0%
-1.0873547791
4.0%
-1.0683526991
4.0%
-0.92545914651
4.0%
-0.86896651981
4.0%
-0.82892954351
4.0%
-0.82240170241
4.0%
-0.80401235821
4.0%
ValueCountFrequency (%)
0.46679911021
4.0%
0.26254704591
4.0%
0.099821232261
4.0%
0.095823876561
4.0%
0.070387974381
4.0%
-0.0068530277351
4.0%
-0.1521488131
4.0%
-0.21408244971
4.0%
-0.47045877581
4.0%
-0.50562638041
4.0%

RQ.EST
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct25
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.266920726
Minimum-2.281232595
Maximum0.4102475941
Zeros0
Zeros (%)0.0%
Negative15
Negative (%)60.0%
Memory size328.0 B
2023-01-06T12:09:04.765187image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-2.281232595
5-th percentile-1.167054534
Q1-0.41892156
median-0.1591620445
Q30.1372242123
95-th percentile0.3102434516
Maximum0.4102475941
Range2.69148019
Interquartile range (IQR)0.5561457723

Descriptive statistics

Standard deviation0.5908985802
Coefficient of variation (CV)-2.213760576
Kurtosis4.628724309
Mean-0.266920726
Median Absolute Deviation (MAD)0.2963862568
Skewness-1.862701636
Sum-6.67301815
Variance0.3491611321
MonotonicityNot monotonic
2023-01-06T12:09:04.821425image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
0.3009398581
 
4.0%
0.18229238691
 
4.0%
0.13171297311
 
4.0%
-0.38687917591
 
4.0%
-0.15916204451
 
4.0%
-0.086416333911
 
4.0%
-0.81874632841
 
4.0%
0.13722421231
 
4.0%
-1.1963086131
 
4.0%
-0.39445579051
 
4.0%
Other values (15)15
60.0%
ValueCountFrequency (%)
-2.2812325951
4.0%
-1.1963086131
4.0%
-1.0500382181
4.0%
-0.81874632841
4.0%
-0.56948184971
4.0%
-0.47270697361
4.0%
-0.418921561
4.0%
-0.39445579051
4.0%
-0.38687917591
4.0%
-0.27485358721
4.0%
ValueCountFrequency (%)
0.41024759411
4.0%
0.312569351
4.0%
0.3009398581
4.0%
0.21965555851
4.0%
0.18229238691
4.0%
0.14170894031
4.0%
0.13722421231
4.0%
0.13171297311
4.0%
0.087893672291
4.0%
0.057269349691
4.0%

VA.EST
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct25
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.5189950524
Minimum-1.924112439
Maximum0.4183469713
Zeros0
Zeros (%)0.0%
Negative19
Negative (%)76.0%
Memory size328.0 B
2023-01-06T12:09:04.881446image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-1.924112439
5-th percentile-1.342217517
Q1-0.9475579262
median-0.4935461283
Q3-0.04231302068
95-th percentile0.3179938972
Maximum0.4183469713
Range2.34245941
Interquartile range (IQR)0.9052449055

Descriptive statistics

Standard deviation0.612229645
Coefficient of variation (CV)-1.179644473
Kurtosis-0.4597634802
Mean-0.5189950524
Median Absolute Deviation (MAD)0.4540117979
Skewness-0.2760868731
Sum-12.97487631
Variance0.3748251382
MonotonicityNot monotonic
2023-01-06T12:09:04.944525image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
-0.85762965681
 
4.0%
0.41834697131
 
4.0%
-0.042313020681
 
4.0%
0.28170368081
 
4.0%
-0.75832396751
 
4.0%
-0.3187851311
 
4.0%
-1.0677956341
 
4.0%
-0.32114115361
 
4.0%
-1.1210306881
 
4.0%
-0.810647131
 
4.0%
Other values (15)15
60.0%
ValueCountFrequency (%)
-1.9241124391
4.0%
-1.350544931
4.0%
-1.3089078661
4.0%
-1.1285254961
4.0%
-1.1210306881
4.0%
-1.0677956341
4.0%
-0.94755792621
4.0%
-0.85762965681
4.0%
-0.810647131
4.0%
-0.75832396751
4.0%
ValueCountFrequency (%)
0.41834697131
4.0%
0.31982761621
4.0%
0.31065902111
4.0%
0.28170368081
4.0%
0.13492757081
4.0%
0.12658327821
4.0%
-0.042313020681
4.0%
-0.21159495411
4.0%
-0.3187851311
4.0%
-0.32114115361
4.0%

Interactions

2023-01-06T12:09:01.719324image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:08:46.505855image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:08:47.460505image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:08:48.313749image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:08:49.253849image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:08:50.225876image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:08:51.101130image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:08:52.006346image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:08:52.819884image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:08:53.720850image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:08:54.532375image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:08:55.428525image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:08:56.363532image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:08:57.241264image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:08:58.132908image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:08:58.959065image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:08:59.899641image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:00.863399image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:01.773303image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:08:46.554017image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:08:47.508460image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:08:48.359466image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:08:49.302503image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:08:50.275462image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:08:51.146162image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:08:52.052559image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:08:52.865506image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:08:53.768258image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:08:54.672947image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2023-01-06T12:08:56.410731image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2023-01-06T12:08:58.179853image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:08:59.004051image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:08:59.953339image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2023-01-06T12:09:01.821333image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:08:46.601471image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:08:47.554925image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:08:48.408716image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2023-01-06T12:08:51.192914image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:08:52.097929image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:08:52.911456image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:08:53.812334image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:08:54.718600image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:08:55.523460image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:08:56.459990image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:08:57.329641image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:08:58.226955image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:08:59.049996image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:00.001224image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2023-01-06T12:08:50.383489image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:08:51.237104image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2023-01-06T12:08:53.858652image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:08:54.763130image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:08:55.570350image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2023-01-06T12:08:57.373612image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:08:58.273547image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:08:59.199970image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:00.053648image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:01.012702image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:01.919998image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2023-01-06T12:08:49.453152image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:08:50.434798image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:08:51.287386image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:08:52.192176image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2023-01-06T12:08:53.906909image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:08:54.811849image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2023-01-06T12:09:01.973040image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:08:46.743326image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:08:47.700820image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2023-01-06T12:08:52.242522image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:08:53.050540image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2023-01-06T12:08:55.669904image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2023-01-06T12:08:57.465729image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:08:58.373294image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:08:59.297350image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:00.156450image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:01.112374image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:02.019049image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:08:46.787459image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:08:47.748840image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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Correlations

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Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2023-01-06T12:09:05.137335image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2023-01-06T12:09:05.358306image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2023-01-06T12:09:05.485936image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

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A simple visualization of nullity by column.
2023-01-06T12:09:02.874973image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

populationGDP_constant_2010_USDland_area_km_sqpopulation_%GDP_%land_area_%EF.EFM.OVRL.XDEF.EFM.RANK.XD1.1_ACCESS.ELECTRICITY.TOT1.2_ACCESS.ELECTRICITY.RURALSH.UHC.NOP1.CGSH.UHC.NOP2.TOSH.UHC.OOPC.25.ZSCC.ESTGE.ESTPV.ESTRQ.ESTVA.EST
02908220.01.055439e+10284700.0004310.0001640.0002200.5091771.099.41564999.1165210.00053469500.01.439833-0.712412-0.170738-0.0068530.300940-0.857630
165002231.02.723610e+115108900.0101170.0048710.0039542.1367020.091.30254486.6229590.00053469500.01.439833-0.2461320.269212-0.7174060.2196560.126583
233333789.01.010600e+114463000.0046840.0014460.0033820.7165456.095.17656790.8748550.00053469500.01.439833-0.493130-0.133275-0.470459-0.110854-0.605667
39256037.02.375218e+101553600.0015960.0005420.0011850.5387159.090.40000075.2101190.00053469500.01.439833-0.5336780.3757230.2625470.141709-0.599356
436306796.02.292974e+102005200.0051020.0003280.0015200.3351581.016.2149649.9798200.00053469500.01.439833-1.001575-0.601155-0.868967-0.262374-0.493546
592444183.03.615786e+1010000000.0129900.0005170.0075780.1334998.028.36344715.5164430.00053469500.01.439833-0.608587-0.450274-1.564271-1.050038-1.308908
6113661809.01.049350e+1219439500.0168370.0163390.0150431.6114031.098.91403795.9355020.0014501139000.01.509390-0.3098500.163555-0.8040120.3125690.134928
735074931.02.957621e+105691400.0054590.0005290.0044040.4422273.020.53929711.0274050.00053469500.01.439833-0.897880-0.687376-1.087355-0.274854-0.211595
860151472.02.218950e+115108900.0103740.0050650.0038981.5323029.082.83523675.4486400.000918798000.02.036451-0.3611920.1802250.4667990.0572690.310659
951057189.08.889510e+105793500.0088050.0020290.0044201.5869028.098.98297198.6487400.00053469500.01.439833-1.110137-0.670080-0.152149-0.418922-0.324850

Last rows

populationGDP_constant_2010_USDland_area_km_sqpopulation_%GDP_%land_area_%EF.EFM.OVRL.XDEF.EFM.RANK.XD1.1_ACCESS.ELECTRICITY.TOT1.2_ACCESS.ELECTRICITY.RURALSH.UHC.NOP1.CGSH.UHC.NOP2.TOSH.UHC.OOPC.25.ZSCC.ESTGE.ESTPV.ESTRQ.ESTVA.EST
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